{"id":"https://openalex.org/W1728604336","doi":null,"title":"Scalable kernel methods for machine learning","display_name":"Scalable kernel methods for machine learning","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W1728604336","mag":"1728604336"},"language":"en","primary_location":{"id":"mag:1728604336","doi":null,"is_oa":false,"landing_page_url":"https://repositories.lib.utexas.edu/bitstream/2152/18243/2/kulisb77172.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063459703","display_name":"Inderjit S. Dhillon","orcid":"https://orcid.org/0000-0002-2759-1416","observed_orcids":["https://orcid.org/0000-0002-2759-1416"]},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Inderjit S. Dhillon","raw_affiliation_strings":["The University of Texas, at Austin"],"raw_orcid":"https://orcid.org/0000-0002-2759-1416","affiliations":[{"raw_affiliation_string":"The University of Texas, at Austin","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049134079","display_name":"Brian Kulis","orcid":"https://orcid.org/0000-0002-1704-3838","observed_orcids":["https://orcid.org/0000-0002-1704-3838"]},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brian Kulis","raw_affiliation_strings":["The University of Texas, at Austin"],"raw_orcid":"https://orcid.org/0000-0002-1704-3838","affiliations":[{"raw_affiliation_string":"The University of Texas, at Austin","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.2786000072956085,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.2786000072956085,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.2667999863624573,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.12370000034570694,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel-methods","display_name":"kernel methods","score":0.734000027179718},{"id":"https://openalex.org/keywords/scalable-learning","display_name":"scalable learning","score":0.7020000219345093},{"id":"https://openalex.org/keywords/transductive-learning","display_name":"transductive learning","score":0.8080000281333923},{"id":"https://openalex.org/keywords/memory-efficiency","display_name":"memory efficiency","score":0.4230000078678131},{"id":"https://openalex.org/keywords/graph-cuts","display_name":"graph cuts","score":0.6389999985694885},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"image segmentation","score":0.6690000295639038},{"id":"https://openalex.org/keywords/image-search","display_name":"image search","score":0.7770000100135803},{"id":"https://openalex.org/keywords/object-recognition","display_name":"object recognition","score":0.9929999709129333},{"id":"https://openalex.org/keywords/human-pose-estimation","display_name":"human pose estimation","score":0.9380000233650208},{"id":"https://openalex.org/keywords/3d-reconstruction","display_name":"3D reconstruction","score":0.9399999976158142},{"id":"https://openalex.org/keywords/semi-supervised-clustering","display_name":"semi-supervised clustering","score":0.9210000038146973},{"id":"https://openalex.org/keywords/large-scale-data","display_name":"large-scale data","score":0.5789999961853027}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7557135820388794},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.700564980506897},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.6599329113960266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.593551516532898},{"id":"https://openalex.org/C55851704","wikidata":"https://www.wikidata.org/wiki/Q7623983","display_name":"String kernel","level":5,"score":0.5648592710494995},{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.5112624168395996},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.48925256729125977},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.4890223443508148},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.44137316942214966},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.4300428628921509},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.4128142297267914},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3213244080543518},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11281630396842957},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.09648075699806213},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:1728604336","doi":null,"is_oa":false,"landing_page_url":"https://repositories.lib.utexas.edu/bitstream/2152/18243/2/kulisb77172.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":107,"referenced_works":["https://openalex.org/W1510073064","https://openalex.org/W1512098439","https://openalex.org/W1521879716","https://openalex.org/W1522930108","https://openalex.org/W1526146785","https://openalex.org/W1537145174","https://openalex.org/W1548698874","https://openalex.org/W1554663460","https://openalex.org/W1560724230","https://openalex.org/W1563088657","https://openalex.org/W1576520375","https://openalex.org/W1584444527","https://openalex.org/W1596382552","https://openalex.org/W1611682757","https://openalex.org/W1631356911","https://openalex.org/W1760551737","https://openalex.org/W1823600381","https://openalex.org/W1986007546","https://openalex.org/W1986280275","https://openalex.org/W2003677307","https://openalex.org/W2012833704","https://openalex.org/W2014158063","https://openalex.org/W2017588182","https://openalex.org/W2017641901","https://openalex.org/W2022416612","https://openalex.org/W2033468335","https://openalex.org/W2040870580","https://openalex.org/W2046618236","https://openalex.org/W2051306216","https://openalex.org/W2059448930","https://openalex.org/W2061607408","https://openalex.org/W2068306188","https://openalex.org/W2070232376","https://openalex.org/W2077486649","https://openalex.org/W2078950386","https://openalex.org/W2083206954","https://openalex.org/W2087347434","https://openalex.org/W2091560152","https://openalex.org/W2092554297","https://openalex.org/W2096100960","https://openalex.org/W2096765209","https://openalex.org/W2097939965","https://openalex.org/W2098651744","https://openalex.org/W2099918640","https://openalex.org/W2100658528","https://openalex.org/W2101098151","https://openalex.org/W2102486516","https://openalex.org/W2104752854","https://openalex.org/W2104978738","https://openalex.org/W2105433369","https://openalex.org/W2105608756","https://openalex.org/W2106435568","https://openalex.org/W2117154949","https://openalex.org/W2118393783","https://openalex.org/W2118953734","https://openalex.org/W2118979615","https://openalex.org/W2121837714","https://openalex.org/W2121853761","https://openalex.org/W2121947440","https://openalex.org/W2122090912","https://openalex.org/W2122808326","https://openalex.org/W2123564153","https://openalex.org/W2125687218","https://openalex.org/W2127218421","https://openalex.org/W2129156852","https://openalex.org/W2129932701","https://openalex.org/W2130556178","https://openalex.org/W2131448432","https://openalex.org/W2132603077","https://openalex.org/W2135674549","https://openalex.org/W2135957668","https://openalex.org/W2137137397","https://openalex.org/W2137557016","https://openalex.org/W2139850885","https://openalex.org/W2139956879","https://openalex.org/W2140095548","https://openalex.org/W2141465109","https://openalex.org/W2141585940","https://openalex.org/W2142623206","https://openalex.org/W2143075842","https://openalex.org/W2145295623","https://openalex.org/W2147717514","https://openalex.org/W2148894497","https://openalex.org/W2152010828","https://openalex.org/W2152144380","https://openalex.org/W2152666411","https://openalex.org/W2152926413","https://openalex.org/W2153839362","https://openalex.org/W2154683974","https://openalex.org/W2155754954","https://openalex.org/W2155904486","https://openalex.org/W2156598602","https://openalex.org/W2159482845","https://openalex.org/W2159583439","https://openalex.org/W2159788726","https://openalex.org/W2160484893","https://openalex.org/W2161455936","https://openalex.org/W2162254475","https://openalex.org/W2162881463","https://openalex.org/W2163290928","https://openalex.org/W2165828254","https://openalex.org/W2165874743","https://openalex.org/W2170536761","https://openalex.org/W2295665070","https://openalex.org/W2798909945","https://openalex.org/W2912210943","https://openalex.org/W3017143921"],"related_works":["https://openalex.org/W6961945501","https://openalex.org/W2246823984","https://openalex.org/W1526741802","https://openalex.org/W1982150766","https://openalex.org/W2535206775","https://openalex.org/W586264559","https://openalex.org/W1542408279","https://openalex.org/W2618572683","https://openalex.org/W2145763471","https://openalex.org/W2408432900"],"abstract_inverted_index":{"Machine":[0],"learning":[1,51,68,78,223],"techniques":[2,359],"are":[3,276],"now":[4],"essential":[5],"for":[6,40,60,74,101,176,222,249,319,346],"a":[7,38,58,92,144,196,219,250,309,323],"diverse":[8],"set":[9],"of":[10,31,62,94,120,164,198,242,244,252,281,288,316,353],"applications":[11,26,200,292],"in":[12,66,89,99,117,161,201,213,278],"computer":[13,202],"vision,":[14],"natural":[15],"language":[16],"processing,":[17],"software":[18],"analysis,":[19],"and":[20,28,43,69,84,191,204,255,291,305,332,377],"many":[21,75],"other":[22,205],"domains.":[23,206],"As":[24,308],"more":[25],"emerge":[27],"the":[29,63,86,118,158,162,193,232,261,264,279,314,333,342],"amount":[30],"data":[32,121,130,165,240,282],"continues":[33],"to":[34,53,103,126,128,143,151,173,189,195,237,269,293,341,374,380],"grow,":[35],"there":[36],"is":[37,147,155,351],"need":[39],"increasingly":[41],"powerful":[42],"scalable":[44,220],"techniques.":[45,259],"Kernel":[46],"methods,":[47,91,371],"which":[48,350],"generalize":[49],"linear":[50,277],"methods":[52,110,134,168,318],"non-linear":[54],"ones,":[55],"have":[56,70,169],"become":[57],"cornerstone":[59],"much":[61],"recent":[64,87],"work":[65],"machine":[67,77],"been":[71,171],"used":[72,248,268],"successfully":[73],"core":[76],"tasks":[79],"such":[80,297],"as":[81,153,298],"clustering,":[82,166],"classification,":[83],"regression.\r\nDespite":[85],"popularity":[88],"kernel":[90,109,133,139,167,214,224,317,335],"number":[93,119,197,280],"issues":[95],"must":[96],"be":[97,174,247,267],"tackled":[98],"order":[100],"them":[102],"succeed":[104],"on":[105,136,157,227],"large-scale":[106],"data.":[107,159,233],"First,":[108,216],"typically":[111],"require":[112],"memory":[113,274],"that":[114],"grows":[115],"quadratically":[116],"objects,":[122,245],"making":[123],"it":[124,154,373],"difficult":[125],"scale":[127],"large":[129,239],"sets.":[131],"Second,":[132],"depend":[135],"an":[137],"appropriate":[138],"function\u2014an":[140],"implicit":[141],"mapping":[142],"high-dimensional":[145],"space\u2014which":[146],"not":[148,170],"clear":[149],"how":[150],"choose":[152],"dependent":[156],"Third,":[160],"context":[163],"demonstrated":[172],"practical":[175],"real-world":[177],"clustering":[178,382],"problems.":[179],"\r\nThis":[180],"thesis":[181],"explores":[182],"these":[183],"questions,":[184],"offers":[185],"some":[186],"novel":[187],"solutions":[188],"them,":[190],"applies":[192],"results":[194],"challenging":[199],"vision":[203],"We":[207,284,321,365],"explore":[208,286,313,378],"two":[209],"broad":[210],"fundamental":[211],"problems":[212],"methods.":[215],"we":[217,312],"introduce":[218],"framework":[221,235,290],"functions":[225,331],"based":[226],"incorporating":[228],"prior":[229],"knowledge":[230],"from":[231],"This":[234,338],"scales":[236],"very":[238],"sets":[241],"millions":[243],"can":[246,266],"variety":[251],"complex":[253],"data,":[254],"outperforms":[256],"several":[257,327],"existing":[258,357,370],"In":[260],"transductive":[262],"setting,":[263],"method":[265],"learn":[270],"low-rank":[271],"kernels,":[272],"whose":[273],"requirements":[275],"points.":[283],"also":[285],"extensions":[287,379],"this":[289,367],"image":[294,375],"search":[295],"problems,":[296],"object":[299],"recognition,":[300],"human":[301],"body":[302],"pose":[303],"estimation,":[304],"3-d":[306],"reconstructions.":[307],"second":[310],"problem,":[311],"use":[315],"clustering.":[320],"show":[322],"mathematical":[324],"equivalence":[325,339],"between":[326],"graph":[328,348],"cut":[329],"objective":[330],"weighted":[334,347],"k-means":[336],"objective.":[337],"leads":[340],"first":[343],"eigenvector-free":[344],"algorithm":[345,368],"cuts,":[349],"thousands":[352],"times":[354],"faster":[355],"than":[356],"state-of-the-art":[358],"while":[360],"using":[361],"significantly":[362],"less":[363],"memory.":[364],"benchmark":[366],"against":[369],"apply":[372],"segmentation,":[376],"semi-supervised":[381]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-09-26T08:19:03.415552","created_date":"2016-06-24T00:00:00"}